Systems and methods for optimizing voice detection via a network microphone device are disclosed herein. In one example, individual microphones of a network microphone device detect sound. The sound data is captured in a first buffer and analyzed to detect a trigger event. Metadata associated with the sound data is captured in a second buffer and provided to at least one network device to determine at least one characteristic of the detected sound based on the metadata. The network device provides a response that includes an instruction, based on the determined characteristic, to modify at least one performance parameter of the NMD. The NMD then modifies the at least one performance parameter based on the instruction.

A sound pickup method obtains a correlation between a first sound pickup signal of a directional first microphone and a second sound pickup signal of a non-directional second microphone, and performs level control of the first sound pickup signal or the second sound pickup signal according to a calculation result of the correlation.

Estimating the field strength from an ultrasonic phased array can be done by summing the contribution of each transducer to the point of interest. Since this contribution is already calculated when creating a converging spherical wave, it can be reused to add a virtual microphone to the system. By monitoring this microphone and moving it along with new focus points, a robust system of field estimates and regulation may be established.

The invention discloses a test circuit, a test method and an audio codec for testing a microphone module that includes a first microphone and a second microphone. The first microphone outputs the first data, and the second microphone outputs the second data. The test circuit includes a comparison circuit, a counter, and a decision circuit. The comparison circuit is configured to compare the first data with the second data and generate a comparison result. The counter is coupled to the comparison circuit and configured to generate a count value based on the comparison result. The decision circuit is coupled to the counter and configured to indicate, based on the count value and a threshold value, whether the microphone module has an error.

Systems and methods for optimizing voice detection via a network microphone device are disclosed herein. In one example, individual microphones of a network microphone device detect sound. The sound data is captured in a first buffer and analyzed to detect a trigger event. Metadata associated with the sound data is captured in a second buffer and provided to at least one network device to determine at least one characteristic of the detected sound based on the metadata. The network device provides a response that includes an instruction, based on the determined characteristic, to modify at least one performance parameter of the NMD. The NMD then modifies the at least one performance parameter based on the instruction.

Embodiments of wireless audio systems and methods for wirelessly communicating audio are disclosed herein. In one example, a method for generating a 3D audio representation of an audio is disclosed. The method includes collecting, by a first wireless headphone, a first audio signal of the audio and generating, by the first wireless headphone, a first synchronizing signal based on a local clock of the first wireless headphone. The method also includes collecting, by a second wireless headphone, a second audio signal of the audio and generating, by the second wireless headphone, a second synchronizing signal based on a local clock of the second wireless headphone. The method yet includes generating, by a user equipment, the 3D audio representation of the audio based on the first and the second audio signals and the first and the second synchronizing signals.

The present disclosure discloses a method and an apparatus for testing a speaker, an electronic device and a storage medium. A specific implementation includes: obtaining first audio data recorded by a microphone integrated with the speaker in ambient white noise; analyzing the first audio data to derive a first analysis result; and determining whether there is a defect in the microphone according to the first analysis result. Hence, these allow for testing a completed set on an assembled speaker to ensure the consistency of a microphone test and improve the accuracy of the test result.

Implementations can detect respective audio data that captures an acoustic event at multiple assistant devices in an ecosystem that includes a plurality of assistant devices, process the respective audio data locally at each of the multiple assistant devices to generate respective measures that are associated with the acoustic event using respective event detection models, process the respective measures to determine whether the detected acoustic event is an actual acoustic event, and cause an action associated with the actional acoustic event to be performed in response to determining that the detected acoustic event is the actual acoustic event. In some implementations, the multiple assistant devices that detected the respective audio data are anticipated to detect the respective audio data that captures the actual acoustic event based on a plurality of historical acoustic events being detected at each of the multiple assistant devices.

Embodiments described herein provide a combined multi-source time difference of arrival (TDOA) tracking and voice activity detection (VAD) mechanism that is applicable for generic array geometries, e.g., a microphone array that lies on a plane. The combined multi-source TDOA tracking and VAD mechanism scans the azimuth and elevation angles of the microphone array in microphone pairs, based on which a planar locus of physically admissible TDOAs can be formed in the multi-dimensional TDOA space of multiple microphone pairs. In this way, the multi-dimensional TDOA tracking reduces the number of calculations that was usually involved in traditional TDOA by performing the TDOA search for each dimension separately.

Embodiments described herein provide a combined multi-source time difference of arrival (TDOA) tracking and voice activity detection (VAD) mechanism that is applicable for generic array geometries, e.g., a microphone array that lies on a plane. The combined multi-source TDOA tracking and VAD mechanism scans the azimuth and elevation angles of the microphone array in microphone pairs, based on which a planar locus of physically admissible TDOAs can be formed in the multi-dimensional TDOA space of multiple microphone pairs. In this way, the multi-dimensional TDOA tracking reduces the number of calculations that was usually involved in traditional TDOA by performing the TDOA search for each dimension separately.